Crross correlation between neural network time series

`nncorr(a,b,maxlag,'`

* flag*')

`nncorr(a,b,maxlag,'`

takes
these arguments, * flag*')

`a` | Matrix or cell array, with columns interpreted as timesteps,
and having a total number of matrix rows of |

`b` | Matrix or cell array, with columns interpreted as timesteps,
and having a total number of matrix rows of |

`maxlag` | Maximum number of time lags |

`flag` | Type of normalization (default = |

and returns an `N`

-by-`M`

cell
array where each `{i,j}`

element is a `2*maxlag+1`

length
row vector formed from the correlations of `a`

elements
(i.e., matrix row) `i`

and `b`

elements
(i.e., matrix column) `j`

.

If `a`

and `b`

are specified
with row vectors, the result is returned in matrix form.

The options for the normalization * flag* are:

`'biased'`

— scales the raw cross-correlation by 1/N.`'unbiased'`

— scales the raw correlation by`1/(N-abs(k))`

, where`k`

is the index into the result.`'coeff'`

— normalizes the sequence so that the correlations at zero lag are 1.0.`'none'`

— no scaling. This is the default.

Here the autocorrelation of a random 1-element, 1-sample, 20-timestep signal is calculated with a maximum lag of 10.

a = nndata(1,1,20) aa = nncorr(a,a,10)

Here the cross-correlation of the first signal with another random 2-element signal are found, with a maximum lag of 8.

b = nndata(2,1,20) ab = nncorr(a,b,8)

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